The present invention relates to exterior inspection for detecting defects of patterns being examined, and particularly to a defect inspection method and apparatus for inspecting patterns in a semiconductor wafer or liquid crystal display.
In a conventional inspection apparatus of this kind, as disclosed in JP-A-55-74409, an image sensor such as a line sensor is used to detect the image of a pattern being examined while the pattern is being moved, and the detected image signal is compared in its gradation with another image signal delayed by a predetermined time, so that the inconsistency in the comparison can be recognized as a defect.
In addition, in another example disclosed in JP-2B-8-10463, two images are arranged in a row and compared with each other.
The above conventional defect recognition methods will be described in detail with reference to FIGS. 1, 2, 3 and 4. FIG. 1 is a schematic diagram of memory mats and peripheral circuits in a memory chip of the pattern being inspected in the prior art. FIG. 2 is a histogram of the brightness of the memory mats and peripheral circuits of the memory chip shown in FIG. 1.
FIG. 3 is a schematic diagram of a pattern being examined which pattern is processed to be flat by CMP (chemical mechanical).
A semiconductor wafer has formed thereon a large number of memory chips 20 one of which is illustrated in FIG. 1. The memory chip 20 can be divided roughly into memory mats 21 and peripheral circuits 22. Each of the memory mats 21 is a group of small repetitive patterns (cells), and the peripheral circuits 22 are fundamentally a group of random patterns. In most cases, if each memory mat is observed in detail, it can be recognized as a group of a plurality of patters repeated at different cell pitches.
FIG. 2 illustrates the distribution of the brightness of the memory mats 21 and peripheral circuits 22 in FIG. 1, or the frequency (histogram) with respect to the brightness of a memory chip expressed by ten bits, or in 1024 gradations, maximum. The memory mats 21 have a high pattern density and are generally dark. The peripheral circuits 22 have a low pattern density and are generally bright.
In the flattening process such as CMP shown in FIG. 3, the circuit pattern within the memory mat 21 changes the brightness with the pattern thickness as will be understood from the histogram of FIG. 4. This figure shows that the wiring layers are deposited and then flattened by CMP. In this pattern, the film thickness locally changes, easily causing irregular brightness. In the case of such a pattern, the brightness values on the pattern shown in FIGS. 2 and 3 are compared. If a threshold is set not to erroneously detect the brightness difference, the sensitivity to defect detection is extremely reduced. This brightness difference can be cancelled out to some extent if a wide wavelength band is used for illumination. However, because the pattern after CMP has sometimes a great change in brightness, there is a limit. Therefore, it has been desired to devise means for detecting minute defects from a pattern having irregular brightness.
Also, in a conventional example, the sum of the squares of the differences between corresponding parts of two pictures is calculated and applied to a paraboloid so that a positional shift between the pictures can be detected. This method, however, does not assure that the two images to be compared are coincident. Thus, optimum matching has been desired for the comparison. FIG. 5 shows experimental results of calculating the sum of the squares of the differences of opposite pixels of two pictures (f(x, y) in FIG. 13 in the later description) of which one picture is shifted by ±1 pixel in the x and y directions. The abscissa indicates the x direction, and the ordinate the y direction. Each value illustrated in the figure is the sum of the squares of the differences. Here, the same pictures (f(x, y) in FIG. 13) are used. That is, Σ(f(x, y)−f(x±1, y±1))2 is calculated as the sum of the squares of the differences. From FIG. 5 it will be seen that the sums of the squares of the differences even between the same pictures are not symmetrical with respect to the center (0, 0), or have an asymmetry of about 0.6%. Since the same pictures one of which is shifted are used, the sum of the squares of the differences is 0 at the point (0, 0). Therefore, even if the position where the sum of the squares of the differences is the minimum is calculated with a resolution of pixel size or below by applying a paraboloid to this data, a correct positional shift, or (0, 0) here cannot be detected.
Also, brightness is changed on the wafer after the flattening process such as CMP. The effect of this brightness change is illustrated in FIG. 6. Here, two pictures are used one of which has 1.1 times the brightness of the other. The brightness 1.1 times higher corresponds to the usual brightness change on the CMP wafer or below. Each value in the experimental results of FIG. 6 is the sum of the absolute values of the differences. The position where the minimum value is located is (0, 1). Thus there is a great error in terms of pixel level contrary to the resolution of pixel or below. The sum of the squares of the differences has the same tendency. From these data, it will be understood that the positional shift between pictures cannot be found precisely. Of course, for the brightness 1.05 times higher there is the same tendency. Thus, applying a paraboloid to the sum of the squares of the differences and calculating the position where the minimum value is obtained must be said to be means having very large error.